This study presents Q2SV, an FPGA‐based quantum state vector simulator implemented using high‐level synthesis (HLS), capable of simulating quantum circuits with up to 29 qubits. Using FPGA parallelism, efficient memory allocation, and hardware‐optimized execution pipelines, Q2SV achieves scalable quantum simulation without requiring iterative resynthesis. The system’s workflow processes OpenQASM circuits by providing a flexible and general‐purpose quantum state vector simulation approach. Experimental evaluations demonstrate significant reductions in execution time and storage requirements, positioning FPGAs as a possible alternative to GPUs and CPUs for large‐scale quantum circuit emulation. While not yet matching the raw computational power of high‐end CPUs and GPUs in all cases, this work establishes a foundational framework for future optimizations in hardware‐accelerated quantum simulation. This work provides an adaptable HLS‐based code that serves as a template that paves the way for enhanced memory management, parallel processing, and architecture‐specific optimizations, enabling more efficient FPGA‐based quantum simulations in the future.
This study presents Q2SV, an FPGA‐based quantum state vector simulator implemented using high‐level synthesis (HLS), capable of simulating quantum circuits with up to 29 qubits. Using FPGA parallelism, efficient memory allocation, and hardware‐optimized execution pipelines, Q2SV achieves scalable quantum simulation without requiring iterative resynthesis. The system’s workflow processes OpenQASM circuits by providing a flexible and general‐purpose quantum state vector simulation approach. Experimental evaluations demonstrate significant reductions in execution time and storage requirements, positioning FPGAs as a possible alternative to GPUs and CPUs for large‐scale quantum circuit emulation. While not yet matching the raw computational power of high‐end CPUs and GPUs in all cases, this work establishes a foundational framework for future optimizations in hardware‐accelerated quantum simulation. This work provides an adaptable HLS‐based code that serves as a template that paves the way for enhanced memory management, parallel processing, and architecture‐specific optimizations, enabling more efficient FPGA‐based quantum simulations in the future.
ABSTRACT In recent years, quantum emulation has attracted increasing attention as a practical approach for developing and evaluating quantum algorithms. However, existing solutions often face challenges in achieving broad applicability, high performance, and efficient utilization of hardware resources and memory. To address these limitations, this paper presents the high‐performance quantum emulation accelerator (HPQEA), a hardware architecture based on the state‐vector emulation approach. The proposed design incorporates four key techniques: a strict data storage strategy, a superior flexible processing element network, an optimized and parallelized CX swapper, and a high‐bandwidth memory‐based bulk data transfer engine. Implementation and evaluation on the Alveo U280 FPGA platform demonstrate that HPQEA can emulate quantum circuits efficiently while maintaining high fidelity and low mean square error. Experimental results show that for QFT benchmarks, HPQEA achieves up to 21,093 faster execution than the NVIDIA A100 GPU for circuits with fewer than 20 qubits. Moreover, the comparison with FPGA‐based related works shows promising results with significant speedup in terms of normalize gate speed from 2.77 to 1448.37. In addition, the architecture demonstrates strong energy efficiency, reducing power consumption by up to 15.58 compared to the Intel i9‐10940X CPU and 80.83 compared to the GTX 1080 GPU. These results highlight the scalability, efficiency, and practicality of HPQEA as a hardware platform for accelerating quantum algorithm emulation.
The area of quantum circuit simulation has attracted a lot of attention in recent years. However, due to the exponentially increasing computational costs, assessing and validating these models on large datasets poses significant obstacles. Despite plenty of research in quantum simulation, issues such as memory management, system adaptability, and execution efficiency remain unresolved. In this study, we introduce QEA, a state vector-based hardware accelerator that overcomes these difficulties with four key improvements: optimized memory allocation management, open PE, flexible ALU, and simplified CX swapper. To evaluate QEA's capabilities, we implemented and evaluated it on the AMD Alveo U280 board, which uses only 0.534 W of power. Experimental results show that QEA is extremely flexible, supporting a wide range of quantum circuits, has excellent fidelity, making it appropriate for standard quantum emulators, and outperforms powerful CPUs and related works up to153.16×better in terms of normalized gate speed. This study has considerable potential as a useful approach for quantum emulators in future works.
With the explosion of data sets, classical computing can hardly meet the requirements for efficient data processing. Quantum computing with the capability of massively parallel processing is the key to next-generation computers. However, the state-of-the-art quantum computers still work at the ENIAC level due to the restricted qubits computation and unreliable results with noise. Quantum simulator provides a reliable environment for quantum algorithm designers, though the limited qubit scales induced by the exponential memory allocation (2n+4 bytes for the n-qubit quantum simulator). A simulator operating on over 40 qubits conventionally requires a high-spec platform like a supercomputer with enormous costs of manufacture, power consumption, etc. We propose a quantum simulator working on FPGA with extensible SATA storage named TREFOIL to overcome the constraints of memory shortage. Considering quantum gates’ data transfer pattern, we propose several HLS designs combined with HDL IPs’ interconnection to optimize the inefficiency of SATA data transfer. According to the result, the target designs simulate 35 qubits in approximately 4 to 7 minutes at less than 35W in power consumption. Besides, we also present the extensibility of the entire system without significant performance degradation.
Abstract This paper presents a field-programmable gate array (FPGA) implementation of a quantum circuit simulator capable of processing 30-qubit systems, representing over 1.07 billion quantum states stored in 8 GB of memory. Built on the Xilinx Alveo U55C platform with High Bandwidth Memory (HBM) technology, our system demonstrates deterministic performance through a 16-bank parallel memory distribution architecture integrated with the Qiskit quantum computing framework. We achieved complete verification across all qubit configurations from 1 to 30 qubits, with execution times ranging from 5 milliseconds to approximately 49 minutes. The simulator supports a comprehensive quantum gate library including single-qubit operations (Hadamard, Pauli gates, rotation gates) and multi-qubit gates (CNOT, controlled-Z, Toffoli. Our performance analysis reveals distinct operational regimes where FPGA excels for small-scale circuits (1-12 qubits) but faces scalability challenges beyond 15 qubits when compared to GPU architectures. This work establishes FPGA as a practical platform for quantum algorithm development and circuit verification, particularly valuable for applications requiring deterministic timing and energy-efficient computation. The architecture provides clear pathways for expansion to 31+ qubits through memory configuration enhancements.
The classical simulation of quantum algorithms is a crucial tool for circuit development, testing, and validation. Although acceleration using GPUs significantly reduces simulation time, most high-performance simulators rely on vendor-specific frameworks that target data-center hardware. To broaden access to quantum simulation, this work proposes a vendor-agnostic approach targeting the integrated GPUs commonly found in consumer-grade laptops. A primary challenge in state-vector simulation is its inherently poor spatial locality, which creates a memory bandwidth bottleneck. Consequently, baseline implementations experience a severe degradation in relative GPU speedup as the number of simulated qubits increases. To address this limitation, we introduce a state partitioning optimization that reorganizes the quantum state vector to maximize the last-level cache locality and minimize costly main memory fetches. We evaluate this strategy using a Quantum Phase Estimation algorithm across diverse architectures from Intel, AMD, and Apple. The experimental results demonstrate that the proposed optimization successfully mitigates performance degradation at larger qubit scales. In particular, for a 28-qubit simulation, the optimization reversed a performance deficit on an Intel Core i5, improving the GPU speedup over the CPU from 0.95x to 1.89x, and increased the Apple M1 Pro speedup from 3.71x to 5.88x. Overall, this approach yields consistent execution time improvements, demonstrating the viability of integrated GPUs for efficient quantum circuit simulation.
As the field of quantum computing grows, novel algorithms which take advantage of quantum phenomena need to be developed. As we are currently in the NISQ (noisy intermediate scale quantum) era, quantum algorithm researchers cannot reliably test their algorithms on real quantum hardware, which is still too limited. Instead, quantum computing simulators on classical computing systems are used. In the quantum circuit model, quantum bits (qubits) are operated on by quantum gates. A quantum circuit is a sequence of such quantum gates operating on some number of qubits. A quantum gate applied to a qubit can be controlled by other qubits in the circuit. This applies the gate only to the states which satisfy the required control qubit state. We particularly target FPGAs as our main simulation platform, as these offer potential energy savings when compared to running simulations on CPUs/GPUs. In this work, we present a memory access pattern to optimise the number of iterations that need to be scheduled to execute a quantum gate such that only the iterations which access the required pairs (determined according to the control qubits imposed on the gate) are scheduled. We show that this approach results in a significant reduction in the time required to simulate a gate for each added control qubit. We also show that this approach benefits the simulation time on FPGAs more than CPUs and GPUs and allows to outperform both CPU and GPU platforms in terms of energy efficiency, which is the main factor for scalability of the simulations.